AP Statistics vocabulary.
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| 9391410062 | context | ideally tells who was measured, what was measured, how the data were collected, where the data were collected, and when and why the study was performed | 0 | |
| 9391410063 | data | systematically recorded information, whether numbers or labels, together with its context | 1 | |
| 9391410064 | data table | an arrangement of data in which each row represents a case and each column represents a variable | 2 | |
| 9391410065 | case | an individual about whom or which we have data | 3 | |
| 9391410066 | variable | holds information about the same characteristic for many cases | 4 | |
| 9391410067 | categorical variable | a variable that names categories (whether with words or numerals) | 5 | |
| 9391410068 | quantitative variable | a variable in which the numbers act as numerical values; always has units | 6 | |
| 9391410069 | units | a quantity or amount adopted as a standard of measurement, such as dollars, hours, or grams | 7 | |
| 9391410070 | frequency table | lists the categories in a categorical variable and gives the count or percentage of observations for each category | 8 | |
| 9391410071 | distribution | gives the possible values of the variable and the relative frequency of each value | 9 | |
| 9391410072 | area principle | in a statistical display, each data value should be represented by the same amount of area | 10 | |
| 9391410073 | bar chart | shows a bar representing the count of each category in a categorical variable | 11 | |
| 9391410074 | pie chart | shows how a "whole" divides into categories by showing a wedge of a circle whose area corresponds to the proportion in each category | 12 | |
| 9391410075 | contingency table | displays counts and, sometimes, percentages of individuals falling into named categories on two or more variables; categorizes the individuals on all variables at once, to reveal possible patterns in one variable that may be contingent on the category of the other | 13 | |
| 9391410076 | marginal distribution | the distribution of either variable alone in a contingency table; the counts or percentages are the totals found in the margins (last row or column) of the table | 14 | |
| 9391410077 | conditional distribution | the distribution of a variable restricting the who to consider only a smaller group of individuals | 15 | |
| 9391410078 | independence | variables are said to be this if the conditional distribution of one variable is the same for each category of the other | 16 | |
| 9391410079 | simpson's paradox | when averages are taken across different groups, they can appear to contradict the overall averages | 17 | |
| 9391410080 | distribution | gives the possible values of the variable and the frequency or relative frequency of each value | 18 | |
| 9391410081 | histogram | uses adjacent bars to show the distribution of vales in a quantitative variable; each bar represents the frequency (or relative frequency) of values falling in an interval of values | 19 | |
| 9391410082 | stem-and-leaf display | shows quantitative data values in a way that sketches the distribution of the data | 20 | |
| 9391410083 | dotplot | graphs a dot for each case against a single axis | 21 | |
| 9391410084 | shape | to describe this aspect of a distribution, look for single vs. multiple modes, and symmetry vs. skewness | 22 | |
| 9391410085 | center | a value that attempts the impossible by summarizing the entire distribution with a single number, a "typical" value | 23 | |
| 9391410086 | spread | a numerical summary of how tightly the values are clustered around the "center" | 24 | |
| 9391410087 | mode | a hump or local high point in the shape of the distribution of a variable; the apparent locations of these can change as the scale of a histogram is changed | 25 | |
| 9391410088 | unimodal | having one mode; this is a useful term for describing the shape of a histogram when it's generally mound-shaped | 26 | |
| 9391410089 | bimodal | distributions with two modes | 27 | |
| 9391410090 | multimodal | distributions with more than two modes | 28 | |
| 9391410091 | uniform | a distribution that's roughly flat | 29 | |
| 9391410092 | symmetric | a distribution is this if the two halves on either side of the center look approximately like mirror images of each other | 30 | |
| 9391410093 | tails | the parts of a distribution that typically trail off on either side; they can be characterized as long or short | 31 | |
| 9391410094 | skewed | a distribution is this if it's not symmetric and one tail stretches out farther than the other | 32 | |
| 9391410095 | outliers | extreme values that don't appear to belong with the rest of the data | 33 | |
| 9391410096 | timeplot | displays data that change over time | 34 | |
| 9391410097 | center | summarized with the mean or the median | 35 | |
| 9391410098 | median | the middle value with half of the data above and half below it | 36 | |
| 9391410099 | spread | summarized with the standard deviation, interquartile range, and range | 37 | |
| 9391410100 | range | the difference between the lowest and highest values in a data set | 38 | |
| 9391410101 | quartile | the lower of this is the value with a quarter of the data below it; the upper of this has a quarter of the data above it | 39 | |
| 9391410102 | interquartile range | the difference between the first and third quartiles | 40 | |
| 9391410103 | percentile | the ith ___ is the number that falls above i% of the data | 41 | |
| 9391410104 | 5-number summary | consists of the minimum and maximum, the quartiles Q1 and Q3, and the median | 42 | |
| 9391410105 | boxplot | displays the 5-number summary as a central box with whiskers that extend to the non-outlying data values | 43 | |
| 9391410106 | mean | found by summing all the data values and dividing by the count | 44 | |
| 9391410107 | variance | the sum of squared deviations from the mean, divided by the count minus one | 45 | |
| 9391410108 | standard deviation | the square root of the variance | 46 | |
| 9391410109 | comparing distributions | when doing this, consider their shape, center, and spread | 47 | |
| 9391410110 | shifting | adding a constant to each data value adds the same constant to the mean, the median, and the quartiles, but does not change the standard deviation or IQR | 48 | |
| 9391410111 | rescaling | multiplying each data value by a constant multiplies both the measures of position and the measures of spread by that constant | 49 | |
| 9391410112 | standardizing | done to eliminate units; values can be compared and combined even if the original variables had different units and magnitudes | 50 | |
| 9391410113 | standardized value | value found by subtracting the mean and dividing by the standard deviation | 51 | |
| 9391410114 | normal model | useful family of models for unimodal, symmetric distributions | 52 | |
| 9391410115 | parameter | numerically valued attribute of a model | 53 | |
| 9391410116 | statistic | value calculated from data to summarize aspects of the data | 54 | |
| 9391410117 | z-score | tells how many standard deviations a value is from the mean; have a mean of zero and a standard deviation of one | 55 | |
| 9391410118 | standard normal model | a normal model with a mean of 0 and a standard deviation of 1 | 56 | |
| 9391410119 | 68-95-99.7 rule | in a normal model, about 68% of values fall within 1 standard deviation of the mean, about 95% fall within 2 standard deviations of the mean, and about 99.7% fall within 3 standard deviations of the mean | 57 | |
| 9391410120 | normal percentile | this corresponding to a z-score gives the percentage of values in a standard normal distribution found at that z-score or below | 58 | |
| 9391410121 | normal probability plot | a display to help assess whether a distribution of data is approximately normal; if it is nearly straight, the data satisfy the nearly normal condition | 59 | |
| 9391410122 | changing center and spread | doing this is equivalent to changing its units | 60 | |
| 9391410123 | scatterplots | shows the relationship between two quantitative variables measured on the same cases | 61 | |
| 9391410124 | direction | a positive ____ or association means that, in general, as one variable increases, so does the other; when increases in one variable generally correspond to decreases in the other, the association is negative | 62 | |
| 9391410125 | form | the ____ we care about most is straight | 63 | |
| 9391410126 | strength | a scatterplot shows an association that is this if there is little scatter around the underlying relationship | 64 | |
| 9391410127 | correlation | a numerical measure of the direction and strength of a linear association | 65 | |
| 9391410128 | outlier | a point that does not fit the overall pattern seen in the scatterplot | 66 | |
| 9391410129 | lurking variable | a variable other than x and y that simultaneously affects both variables, accounting for the correlation between the two | 67 | |
| 9391410130 | model | an equation or formula that simplifies and represents reality | 68 | |
| 9391410131 | linear model | an equation of the form y-hat = b0 + b1x | 69 | |
| 9391410132 | residuals | the differences between data values and the corresponding values predicted by the regression model; ____ = observed value - predicted value | 70 | |
| 9391410133 | predicted value | found by substituting the x-value in the regression equation; they're the values on the fitted line | 71 | |
| 9391410134 | slope | gives a value in "y-units per x-unit"; changes of one unit in x are associated with changes of b1 units in predicted values of y | 72 | |
| 9391410135 | regression to the mean | each predicted y-hat tends to be fewer standard deviations from its mean than its corresponding x was from its mean | 73 | |
| 9391410136 | regression line | the linear equation y-hat = b0 + b1x that satisfies the least squares criterion | 74 | |
| 9391410137 | intercept | this, b0, gives a starting value in y-units; it's the y-hat-value when x is 0 | 75 | |
| 9391410138 | least squares | this criterion specifies the unique line that minimizes the variance of the residuals or, equivalently, the sum of the squared residuals | 76 | |
| 9391410139 | r2 | the square of the correlation between y and x; gives the fraction of the variability of y accounted for by the least squares linear regression on x; an overall measure of how successful the regression is in linearly relating y to x | 77 | |
| 9391410140 | subset | if data consist of two or more groups that have been thrown together, it is usually best to fit different linear models to each group than to try to fit a single model to all of the data | 78 | |
| 9391410141 | extrapolation | although linear models provide an easy way to predict values of y for a given value of x, it is unsafe to predict for values of x far from the ones used to find the linear model equation; predictions should not be trusted | 79 | |
| 9391410142 | outlier | any data point that stands away from the others; can be extraordinary by having a large residual or by having high leverage | 80 | |
| 9391410143 | leverage | data points whose x-values are far from the mean of x are said to exert ____ on a linear model; with high enough ____, residuals can appear to be deceptively small | 81 | |
| 9391410144 | influential point | when omitting a point from the data results in a very different regression model, the point is an ____ | 82 | |
| 9391410145 | lurking variable | a variable that is not explicitly part of a model but affects the way the variables in the model appear to be related | 83 | |
| 9391410146 | re-express data | we do this by taking the logarithm, the square root, the reciprocal, or some other mathematical operation on all values in the data set | 84 | |
| 9391410147 | ladder of powers | places in order the effects that many re-expressions have on the data | 85 | |
| 9391410148 | random | an event is this if we know what outcomes could happen, but not which particular values will happen | 86 | |
| 9391410149 | random numbers | these are hard to generate, but several websites offer an unlimited supply of equally likely random values | 87 | |
| 9391410150 | simulation | models random events by using random numbers to specify event outcomes with relative frequencies that correspond to the true real-world relative frequencies we are trying to model | 88 | |
| 9391410151 | simulation component | the most basic situation in a simulation in which something happens at random | 89 | |
| 9391410152 | outcome | an individual result of a component of a simulation | 90 | |
| 9391410153 | trial | the sequence of several components representing events that we are pretending will take place | 91 | |
| 9391410154 | response variable | values of this record the results of each trial with respect to what we were interested in | 92 | |
| 9391410155 | population | the entire group of individuals or instances about whom we hope to learn | 93 | |
| 9391410156 | sample | a representative subset of a population, examined in hope of learning about the population | 94 | |
| 9391410157 | sample survey | a study that asks questions of a sample drawn from some population in the hope of learning something about the entire population | 95 | |
| 9391410158 | bias | any systematic failure of a sampling method to represent its population; common errors are voluntary response, undercoverage, nonresponse ____, and response ____ | 96 | |
| 9391410159 | randomization | the best defense against bias, in which each individual is given a fair, random chance of selection | 97 | |
| 9391410160 | matching | any attempt to force a sample to resemble specified attributes of the population | 98 | |
| 9391410161 | sample size | the number of individuals in a sample | 99 | |
| 9391410162 | census | a sample that consists of the entire population | 100 | |
| 9391410163 | population parameter | a numerically valued attribute of a model for a population | 101 | |
| 9391410164 | representative | a sample is this if the statistics computed from it accurately reflect the corresponding population parameters | 102 | |
| 9391410165 | simple random sample | this of sample size n is one in which each set of n elements in the population has an equal chance of selection | 103 | |
| 9391410166 | sampling frame | a list of individuals from whom the sample is drawn | 104 | |
| 9391410167 | sampling variability | the natural tendency of randomly drawn samples to differ | 105 | |
| 9391410168 | stratified random sample | a sampling design in which the population is divided into several subpopulations, and random samples are then drawn from each stratum | 106 | |
| 9391410169 | cluster sample | a sampling design in which entire groups are chosen at random | 107 | |
| 9391410170 | multistage sample | sampling schemes that combine several sampling methods | 108 | |
| 9391410171 | systematic sample | a sample drawn by selecting individuals systematically from a sampling frame | 109 | |
| 9391410172 | voluntary response bias | bias introduced to a sample when individuals can choose on their own whether to participate in the sample | 110 | |
| 9391410173 | convenience sample | consists of the individuals who are conveniently available | 111 | |
| 9391410174 | undercoverage | a sampling scheme that biases the sample in a way that gives a part of the population less representation than it has in the population | 112 | |
| 9391410175 | nonresponse bias | bias introduced to a sample when a large fraction of those sampled fails to respond | 113 | |
| 9391410176 | response bias | anything in a survey design that influences response | 114 | |
| 9391410177 | observational study | a study based on data in which no manipulation of factors has been employed | 115 | |
| 9391410178 | retrospective study | an observational study in which subjects are selected and then their previous conditions or behaviors are determined | 116 | |
| 9391410179 | prospective study | an observational study in which subjects are followed to observe future outcomes | 117 | |
| 9391410180 | experiment | manipulates factor levels to create treatments, randomly assigns subjects to these treatment levels, and then compares the responses of the subject groups across treatment levels | 118 | |
| 9391410181 | random assignment | to be valid, an experiment must assign experimental units to treatment groups at random | 119 | |
| 9391410182 | factor | a variable whose levels are controlled by the experimenter | 120 | |
| 9391410183 | response | a variable whose values are compared across different treatments | 121 | |
| 9391410184 | experimental units | individuals on whom an experiment is performed | 122 | |
| 9391410185 | level | the specific values that the experimenter chooses for a factor | 123 | |
| 9391410186 | treatment | the process, intervention, or other controlled circumstance applied to randomly assigned experimental units | 124 | |
| 9391410187 | principles of experimental design | control, randomize, replicate, block | 125 | |
| 9391410188 | statistically significant | when an observed difference is too large for us to believe that is is likely to have occurred naturally | 126 | |
| 9391410189 | control group | the experimental units assigned to a baseline treatment level, typically either the default treatment, which is well understood, or a null, placebo treatment | 127 | |
| 9391410190 | blinding | any individual associated with an experiment who is not aware of how subjects have been allocated to treatment groups | 128 | |
| 9391410191 | single-blind | when either those who could influence or evaluate the results is blinded | 129 | |
| 9391410192 | double-blind | when both those who could influence and evaluate the results are blinded | 130 | |
| 9391410193 | placebo | a treatment known to have no effect, administered so that all groups experience the same conditions | 131 | |
| 9391410194 | placebo effect | the tendency of many human subjects (often 20% or more of experiment subjects) to show a response even when administered a placebo | 132 | |
| 9391410195 | block | when groups of experimental units are similar, it is a good idea to gather them together into these | 133 | |
| 9391410196 | matched | in a retrospective or prospective study, subjects who are similar in ways not under study may be ____ and then compared with each other on the variables of interest | 134 | |
| 9391410197 | randomized block design | randomization occurring within blocks | 135 | |
| 9391410198 | completely randomized design | all experimental units have an equal chance of receiving any treatment | 136 | |
| 9391410199 | confounded | when the levels of one factor are associated with the levels of another factor so their effects cannot be separated | 137 |
